Sentiment Analysis on Big News Media Data

Abu-Salih, Bilal; Wongthongtham, Pornpit; Zhu, Dengya; Chan, Kit Yan; Rudra, Amit

doi:10.1007/978-981-33-6652-7_7

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Cited by 3 publications

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“…Dataset2 -Twitter Conversations dataset: We collected around 300 tweets (replies) captured from Twitter conversations of five Twitter accounts which are the official Twitter accounts of certain Australian banks. Replies to tweets that are posted by service providers commonly covey customers' opinions toward provided service [ 41 , 42 ]. Thus, these tweets would carry a rich source of emotions to be analyzed.…”

Section: Methodsmentioning

confidence: 99%

Emotion detection of social data: APIs comparative study

Abu-Salih

Alhabashneh

Zhu

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Emotion detection of social data: APIs comparative study

Abu-Salih

Alhabashneh

Zhu

et al. 2023

Heliyon

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Design of Four Web Crawlers based on Python

Liu

Zheng

et al. 2022

2022 8th International Symposium on System Security, Safety, and Reliability (ISSSR)

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Sentiment Analysis and Summarization of Facebook Posts on News Media

Fung¹,

Lee²,

Chui³

et al. 2022

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media

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Social media has become part of daily life in the modern world. News media companies (NMC) use social network sites including Facebook pages to let net users keep updated. Public expression is important to NMC for making valuable journals, but it is not cost-effective to collect millions of feedback by human effort, which can instead be automated by sentiment analysis. This chapter presents a mobile application called Facemarize that summarizes the contents of news media Facebook pages using sentiment analysis. The sentiment of user comments can be quickly analyzed and summarized with emotion detection. The sentiment analysis achieves an accuracy of over 80%. In a survey with 30 participants including journalists, journalism students, and journalism graduates, the application gets at least 4.9 marks (in a 7-point Likert scale) on the usefulness, ease of use, ease of learning, and satisfaction with a mean reliability score of 3.9 (out of 5), showing the effectiveness of the application.

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Sentiment Analysis on Big News Media Data

Cited by 3 publications

References 28 publications

Emotion detection of social data: APIs comparative study

Emotion detection of social data: APIs comparative study

Design of Four Web Crawlers based on Python

Sentiment Analysis and Summarization of Facebook Posts on News Media

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